Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems
| dc.contributor.author | Tarplee, Kyle M., author | |
| dc.contributor.author | Maciejewski, Anthony A., advisor | |
| dc.contributor.author | Siegel, Howard Jay, committee member | |
| dc.contributor.author | Chong, Edwin, committee member | |
| dc.contributor.author | Bates, Dan, committee member | |
| dc.date.accessioned | 2015-08-28T14:34:56Z | |
| dc.date.available | 2015-08-28T14:34:56Z | |
| dc.date.issued | 2015 | |
| dc.description | Zip file contains data files and readme. | |
| dc.description.abstract | As high performance computing systems increase in size, new and more efficient algorithms are needed to schedule work on the machines, understand the performance trade-offs inherent in the system, and determine which machines to provision. The extreme scale of these newer systems requires unique task scheduling algorithms that are capable of handling millions of tasks and thousands of machines. A highly scalable scheduling algorithm is developed that computes high quality schedules, especially for large problem sizes. Large-scale computing systems also consume vast amounts of electricity, leading to high operating costs. Through the use of novel resource allocation techniques, system administrators can examine this trade-off space to quantify how much a given performance level will cost in electricity, or see what kind of performance can be expected when given an energy budget. Trading-off energy and makespan is often difficult for companies because it is unclear how each affects the profit. A monetary-based model of high performance computing is presented and a highly scalable algorithm is developed to quickly find the schedule that maximizes the profit per unit time. As more high performance computing needs are being met with cloud computing, algorithms are needed to determine the types of machines that are best suited to a particular workload. An algorithm is designed to find the best set of computing resources to allocate to the workload that takes into account the uncertainty in the task arrival rates, task execution times, and power consumption. Reward rate, cost, failure rate, and power consumption can be optimized, as desired, to optimally trade-off these conflicting objectives. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.format.medium | ZIP | |
| dc.format.medium | CSV | |
| dc.format.medium | JSON | |
| dc.format.medium | TXT | |
| dc.identifier | Tarplee_colostate_0053A_12890.pdf | |
| dc.identifier.uri | http://hdl.handle.net/10217/167055 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | high performance computing | |
| dc.subject | resource provisioning | |
| dc.subject | stochastic programming | |
| dc.subject | linear programming | |
| dc.subject | heterogeneous computing | |
| dc.subject | scheduling | |
| dc.title | Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |